A technique for extracting topic from a document group according to a specific classification belongs to a text mining field. The technique has been studied with a technique of summarizing many documents.
For example, the technique analyzes user's interest and characteristic, etc. in accordance with a specific classification, by using an article posted to a social network service such as a twitter and a facebook or various posts provided by a portal enterprise. The analyzed result is useful to make a decision for regional marketing of the enterprise or to establish a government policy.
For example, some services such as the twitter and the facebook provide user's locations in their posts. A daum as a domestic portal enterprise provides a service which collects regional popular news based on location information of a user who searches news provided thereby.
A text data containing the location information may include characteristics such as interest expressed by regional users and figure out difference of the characteristics in those regions according to comparison of the regions.
However, in the conventional technique, it is difficult to determine a parameter value used for extracting the topic. It is impossible to extract accurate topic if proper parameter is not provided.
Accordingly, a technique for increasing accuracy of topic extraction when the topic is extracted from the document group has been required.